In recent history, advances in technology have caused the world to become increasingly integrated and globalized. Many companies are now global entities comprising offices and manufacturing sites geographically dispersed throughout the world. With such an integrated, yet geographically diverse, world people often need to communicate with other parties who are located far away. In order to facilitate such communication teleconferencing and video conferencing are widely used. Teleconferencing connects two or more parties over an audio network. Video conferencing further includes a camera and a video monitor allowing the parties to converse while viewing video images of each other.
Teleconferencing and videoconferencing systems are often used during meetings. During meetings situations often occur in which numerous people using a single teleconferencing device (e.g., a single phone) are talking over each other in a single room. In such situations the sound that is captured (e.g., received) by one or more microphone(s) of the teleconferencing device is a mixture of a plurality of voices and reverberating sounds from around the room. Blind Signal Separation (BSS) relates to the task of separating signals (e.g., sounds) when only their mixtures are observed (e.g., captured). BSS has diverse applications in many fields including vision research, brain imaging, and telecommunications. Of particular interest to this disclosure, in telecommunications BSS can be used to improve the sound quality of captured sound in digital communication such as teleconferencing, voice over IP, computer as a phone, and speech recognition.
Recently, Independent Component Analysis (ICA) has become a popular method of performing BSS. ICA is a computational method for separating a mixture of signals captured (e.g., received) from the plurality of sources into individual components associated with respective sources. For example, in telecommunications, ICA algorithms are designed to receive a mixture of sound (e.g., mixture of voices) output by a plurality of sources (e.g., people) from one or more recording devices (e.g., microphones) dispersed throughout a room (e.g., in the middle of a table) and unmix the captured mixture of sound to recover sound from individual sources without having any information of who the sources are or where they are located.
More particularly, ICA is a form of BSS that supposes a mutual statistical independence of the source signals (e.g., people's voices). When used in a telecommunication system, an ICA algorithm is performed independent of assumptions regarding the room or people using the system. Instead, an ICA algorithm utilizes a simple statistical model to manipulate captured signals so that statistically what comes out of each microphone is a signal that is independent from the signals coming from other microphones.